Mrs. Inajara Rutyna | Online Monitoring | Best Researcher Award

Mrs. Inajara Rutyna | Online Monitoring | Warsaw University of Technology | Poland

Mrs. Inajara Rutyna is a distinguished researcher in the field of Artificial Intelligence and Renewable Energy Systems, currently pursuing her Ph.D. in Automation, Electronics, and Electrical Engineering at the Warsaw University of Technology, Poland. Her academic foundation is built on a Master’s degree in Numerical Methods in Engineering and a Bachelor’s degree in Industrial Mathematics from the Universidade Federal do Paraná, Brazil. Throughout her academic and professional journey, Mrs. Inajara Rutyna has consistently demonstrated exceptional proficiency in mathematical modeling, computational intelligence, and optimization methods. Her professional experience encompasses diverse roles, including AI Development Specialist at IDEAS NCBR Sp. z o.o., where she developed intelligent algorithms and Python-based models for renewable energy forecasting, and Mathematical Modeller and Data Scientist at the National Centre for Nuclear Research, Poland, contributing to mathematical frameworks for sustainable power systems. Additionally, her earlier engagements as a Game Economy Designer at Rage Quit Games and as a Project and Process Analyst at Segula do Brasil Engenharia e Tecnologia reflect her versatility in applying data-driven modeling to industrial, gaming, and energy contexts. Mrs. Rutyna’s research interests lie primarily in Artificial Intelligence applications for renewable energy forecasting, computational fluid dynamics, optimization algorithms, and machine learning-based energy modeling. Her technical skills include advanced programming in Python, MATLAB, and Fortran, as well as expertise in numerical analysis, data science, and algorithmic development. She has authored and co-authored multiple IEEE and Scopus-indexed publications focusing on energy efficiency prediction, evaluation metrics for wind power, and AI-based forecasting. She is an active member of professional bodies such as the IEEE, contributing to international research collaborations and scientific discussions on sustainable technology innovation.

Professional Profiles: ORCID

Featured Publications

Rutyna, I. (n.d.). Gated lag and feature selection for day-ahead wind power forecasting using on-site SCADA data. IEEE. (Citations: 42)
Rutyna, I. (n.d.). Efficiency analysis of k-nearest neighbors machine learning method for 10-minutes ahead forecasts of electric energy production at an onshore wind farm. Elsevier. (Citations: 38)
Rutyna, I. (n.d.). Evaluation metrics for wind power forecasts: A comprehensive review and statistical analysis of errors. IEEE Access. (Citations: 57)
Rutyna, I. (n.d.). Polynomial interpolation with repeated Richardson extrapolation to reduce discretization error in CFD. Springer. (Citations: 31)
Rutyna, I. (n.d.). Stochastic hybrid optimization methods for renewable energy forecasting and grid stability. IEEE Transactions on Sustainable Energy. (Citations: 29)

Dr. Michelangelo Mortello | Monitoring | Excellence in Research Award

Dr. Michelangelo Mortello | Monitoring | Italian Welding Institute | Italy

Dr. Michelangelo Mortello is a highly accomplished researcher and materials engineer at the Istituto Italiano della Saldatura (IIS) – Ente Morale, Genoa, Italy, whose work stands at the intersection of welding technology, materials science, and sensing innovation. With a strong academic foundation culminating in a Ph.D. in Materials Engineering, Dr. Mortello has developed profound expertise in laser-based manufacturing, hybrid welding systems, hydrogen embrittlement mitigation, and subsurface sensing applications for industrial metals. His education and continuous professional engagement have equipped him with exceptional research insight into advanced joining processes, metallurgy, and additive manufacturing. Throughout his career, Dr. Mortello has accumulated an extensive body of work, publishing more than 29 scientific papers with over 800 citations and an h-index of 16, reflecting the global influence of his research contributions. His professional experience spans collaborative roles in advanced laboratories and European research initiatives, where he has contributed to the development of real-time sensing frameworks for process monitoring, structural diagnostics, and non-destructive evaluation. At IIS, he has led and contributed to numerous research projects focused on laser–arc hybrid welding, hydrogen embrittlement studies, and the structural evaluation of pipelines for repurposing energy systems. Dr. Mortello’s research interests encompass a wide spectrum of interdisciplinary domains, including additive manufacturing, laser processing, smart sensing technologies, and the integration of artificial intelligence in material diagnostics. His scientific pursuits aim to enhance the reliability, sustainability, and precision of industrial materials through predictive modeling and sensor-enabled process control. Equipped with advanced research skills such as finite element modeling (FEM), computational simulation, microstructural characterization, and corrosion testing, he consistently contributes innovative solutions to address the challenges of modern manufacturing.

Professional Profile: ORCID | Scopus | Google Scholar

Selected Publications

Mortello, M., & Casalino, G. (2021). Transfer mode effects on Ti6Al4V wall building in wire laser additive manufacturing. Manufacturing Letters. — Citations: 65
Casalino, G., & Mortello, M. (2021). Laser-arc combined welding of AA5754 alloy. Materials Letters. — Citations: 58
Contuzzi, N., Mortello, M., & Casalino, G. (2021). On the laser scarfing of epoxy resin matrix composite with copper reinforcement. Manufacturing Letters. — Citations: 47
Casalino, G., Leo, P., Mortello, M., Perulli, P., & Varone, A. (2017). Effects of laser offset and hybrid welding on microstructure and IMC in Fe–Al dissimilar welding. Metals. — Citations: 83
Casalino, G., Guglielmi, P., Lorusso, V. D., Mortello, M., Peyre, P., & Sorgente, D. (2017). Laser offset welding of AZ31B magnesium alloy to 316 stainless steel. Journal of Materials Processing Technology. — Citations: 112